Re-thinking the environment in landscape genomics
Authors: Benjamin Dauphin, Christian Rellstab, Rafael O. Wüest, Dirk N. Karger, Rolf Holderegger, Felix Gugerli, Stéphanie Manel
Journal: Trends in Ecology & Evolution (2022)

The increasing availability of new, high-quality geo-referenced environmental data(bases) is stimulating landscape genomic studies of terrestrial and aquatic organisms.Environmental data (e.g., climate, soil, and topography) are now available at multiple spatial and temporal scales and, together with environmentally and genetically informed sampling designs, enable us to capture selection pressures at high resolution in various organisms.Statistical advances in genotype–environment association methods now allow testing the response of population genomic variation to complex environments, using nonredundant and informative environmental predictors.Our understanding of the environmental constraints underlying local adaptation of living organisms has provided insights into the potential responses of populations to environmental changes such as global warming. This understanding is a key component of well-informed biodiversity conservation programmes. Detecting the extrinsic selective pressures shaping genomic variation is critical for a better understanding of adaptation and for forecasting evolutionary responses of natural populations to changing environmental conditions. With increasing availability of geo-referenced environmental data, landscape genomics provides unprecedented insights into how genomic variation and underlying gene functions affect traits potentially under selection. Yet, the robustness of genotype–environment associations used in landscape genomics remains tempered due to various limitations, including the characteristics of environmental data used, sampling designs employed, and statistical frameworks applied. Here, we argue that using complementary or new environmental data sources and well-informed sampling designs may help improve the detection of selective pressures underlying patterns of local adaptation in various organisms and environments. Detecting the extrinsic selective pressures shaping genomic variation is critical for a better understanding of adaptation and for forecasting evolutionary responses of natural populations to changing environmental conditions. With increasing availability of geo-referenced environmental data, landscape genomics provides unprecedented insights into how genomic variation and underlying gene functions affect traits potentially under selection. Yet, the robustness of genotype–environment associations used in landscape genomics remains tempered due to various limitations, including the characteristics of environmental data used, sampling designs employed, and statistical frameworks applied. Here, we argue that using complementary or new environmental data sources and well-informed sampling designs may help improve the detection of selective pressures underlying patterns of local adaptation in various organisms and environments. The environment, and in particular its spatio-temporal changes, exerts strong selection pressures on fitness-related phenotypic traits. If these traits are genetically controlled, natural selection leads to locally adapted populations. Thus, environmental drivers leave a specific signature in the genomes of species and populations. This enables the inference of local adaptation without directly measuring fitness traits, but instead determining the effect of environmental factors (see Glossary) on allele frequencies using landscape genomic methods such as genotype–environment associations (GEA) [1.Rellstab C. et al.A practical guide to environmental association analysis in landscape genomics.Mol. Ecol. 2015; 24: 4348-4370Crossref PubMed Scopus (412) Google Scholar]. The research field of landscape genomics contributes to the understanding of the genomic mechanisms underlying local adaptation, while detecting the environmental factors driving it. Landscape genomic inference can be expanded to assess the possible maladaptation of populations to environmental change (genomic offset [2.Capblancq T. et al.Genomic prediction of (mal)adaptation across current and future climatic landscapes.Annu. Rev. Ecol. Evol. Syst. 2020; 51: 245-269Crossref Scopus (98) Google Scholar,3.Rellstab C. et al.Prospects and limitations of genomic offset in conservation management.Evol. Appl. 2021; 14: 1202-1212Crossref PubMed Scopus (35) Google Scholar]). This knowledge is particularly valuable in the context of human-induced environmental alterations, such as climate or land-use change, and is therefore relevant for nature conservation and ecosystem management [4.Layton K.K.S. et al.Genomic evidence of past and future climate-linked loss in a migratory Arctic fish.Nat. Clim. Chang. 2021; 11: 158-165Crossref Scopus (24) Google Scholar,5.Bay R.A. et al.Genomic signals of selection predict climate-driven population declines in a migratory bird.Science (1979). 2018; 359: 83-86Crossref PubMed Scopus (237) Google Scholar]. In fact, the concept of genomic offset can inform assisted gene flow and migration strategies that are now at the heart of conservation programmes to support climate-threatened populations and strengthen ecosystem resilience [6.Hoffmann A.A. et al.Genetic mixing for population management: from genetic rescue to provenancing.Evol. Appl. 2021; 14: 634-652Crossref PubMed Scopus (57) Google Scholar]. The field of landscape genomics emerged about 15 years ago with the publication of the first conceptual approach specifically designed for GEA analysis [7.Joost S. et al.A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation.Mol. Ecol. 2007; 16: 3955-3969Crossref PubMed Scopus (332) Google Scholar]. Thereafter, the advent of next-generation sequencing techniques and large geo-referenced environmental datasets has dramatically increased the quality and quantity of both genomic resources and environmental factors [8.Xia J. et al.Research challenges and opportunities for using big data in global change biology.Glob. Chang. Biol. 2020; 26: 6040-6061Crossref PubMed Scopus (33) Google Scholar]. Consequently, the field of landscape genomics has grown rapidly. According to our literature survey (n = 278; Figure 1A ; see the supplemental information online for methodological details), more than 50% of all landscape genomic articles published during the survey period 2007–2021 were issued after 2017. A majority of studies initially focused on plants (especially trees), likely due to their sedentariness. This trend has become more balanced in recent years, resulting in 51% and 47% of all studies (2007–2021) examining plant and animals, respectively (Figure 1A). Landscape genomics has also expanded to the aquatic world (termed seascape or riverscape genomics [9.Liggins L. et al.Seascape genomics: contextualizing adaptive and neutral genomic variation in the ocean environment.in: Oleksiak M.F. Rajora O.P. Population Genomics: Marine Organisms. 1st edn. Springer, 2020: 171-218Google Scholar,10.Grummer J.A. et al.Aquatic landscape genomics and environmental effects on genetic variation.Trends Ecol. Evol. 2019; 34: 641-654Abstract Full Text Full Text PDF PubMed Scopus (78) Google Scholar] as subfields of landscape genomics), among which marine studies have become particularly frequent in the past 2 years. This development is mirrored by the three most studied ecosystems [forest (32.7%), marine (13.3%), and agriculture (10.8%); Figure 1A], which together account for 56.8% of all studies. A landscape genomic study typically relies on: (i) an appropriate sampling design to incorporate intraspecific genetic diversity while capturing relevant environmental differences, (ii) geo-referenced environmental data that accurately describe the putative selective pressures of interest acting on populations or individuals, (iii) high-quality genome-wide data, and (iv) appropriate statistical tools to correlate response (genomics, Y) and predictor (environmental, X) variables, while accounting for confounding effects such as neutral genetic structure [11.Lotterhos K.E. Whitlock M.C. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method.Mol. Ecol. 2015; 24: 1031-1046Crossref PubMed Scopus (321) Google Scholar]. As a result, manifold decisions need to be made at an early stage of a study (Figure 2). While there are numerous overviews on methodological issues and genomic approaches (e.g., [12.Selmoni O. et al.Sampling strategy optimization to increase statistical power in landscape genomics: a simulation-based approach.Mol. Ecol. Resour. 2020; 20: 154-169Crossref PubMed Scopus (22) Google Scholar, 13.Wold J. et al.Expanding the conservation genomics toolbox: incorporating structural variants to enhance genomic studies for species of conservation concern.Mol. Ecol. 2021; 30: 5949-5965Crossref PubMed Scopus (14) Google Scholar, 14.Bourgeois Y.X.C. Warren B.H. An overview of current population genomics methods for the analysis of whole-genome resequencing data in eukaryotes.Mol. Ecol. 2021; 30: 6036-6071Crossref PubMed Scopus (23) Google Scholar]), there are hitherto no general guidelines on the use of environmental data in landscape genomics. Here, we fill this gap and review the types and applications of environmental data in landscape genomics and highlight promising avenues for better characterising environmental factors capturing selection pressures from complex and heterogeneous habitats, with the aim of improving the robustness of landscape genomic outcomes. Notably, our literature survey revealed that only 36% of all studies reported values of explanatory power of the presented GEA models. Always taking the best model of each study, the median explanatory power across all studies resulted in an R2 of 0.38 (n = 93, standard deviation = 0.29) with values ranging from 0.01 to 0.98 and an overall trend towards low R2 values in studies with small sample sizes (Figure 1B). Although the maximum explanatory power achievable with such models remains unknown, these values indicate that landscape genomic analyses have revealed only moderate explanatory power yet. Hence, we argue that better informed decisions, for example, based on prior knowledge of environmental data (i.e., choice, type, source, and scale of environmental factors as predictors; Figure 2), should guide the sampling design and anticipate statistical limitations, which may enhance the confidence in future landscape genomic studies. To this end, we highlight trends in the literature and present four main avenues that can be pursued to improve GEA models. Researchers have three main data sources to describe the environmental conditions at sampling locations: in situ measurements, remote sensing, or spatial interpolation (Figure 2), the latter being clearly the most widely used to date (e.g., WorldClim; [15.Fick S.E. Hijmans R.J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas.Int. J. Climatol. 2017; 37: 4302-4315Crossref Scopus (6863) Google Scholar]). The open access and user-friendly design of large interpolation-based, geo-referenced environmental databases have greatly improved the characterisation and exploration of ecological gradients relevant to identify selective pressures in natural populations. Their convenience is that no additional environmental data has to be collected in the field. The availability of such big data has been facilitated by initiatives led by international consortia to acquire, model, store, and share environmental data on a global scale. For example, the World Ocean Database [16.Boyer T.P. et al.World Ocean Database.in: Mishonov A.V. NOAA Atlas NESDIS. 87. NOAA, 2018: 1-207Google Scholar], powered by over 20 000 datasets, centralises a coordinated effort of uniformly curated data for oceanographic, climate, and environmental research. With such resources, ambitious studies have tackled the genomics of adaptation over continental-scale areas (e.g., [17.Babin C. et al.RAD-Seq reveals patterns of additive polygenic variation caused by spatially-varying selection in the American eel (Anguilla rostrata).Genome Biol. Evol. 2017; 9: 2974-2986Crossref PubMed Scopus (28) Google Scholar]; Figure 1C), leaving sampling effort and the cost of genomic analyses as the main bottleneck. Nevertheless, further efforts in data acquisition and interpolation modelling are still needed to better grasp environmental variation, particularly in poorly sampled areas of freshwater and marine ecosystems. In parallel, highly accurate pocket-sized devices have been developed to measure biophysical properties at sampling locations, allowing in situ measurements with high spatial resolution and temporal grain. These are powerful tools for studying the local habitats of living organisms, especially for those with short generation times and strong population dynamics associated with selection pressures. However, in situ measurements often represent short snapshots of environmental conditions and may inadequately reflect the long-term selection pressure within an evolutionary timescale. Nearly 90% of landscape genomic studies focused on abiotic factors as potential selective pressures, largely neglecting biotic factors that may be of adaptive relevance (Figure 1D; but see [18.Maselko J. et al.Long-lived marine species may be resilient to environmental variability through a temporal portfolio effect.Ecol. Evol. 2020; 10: 6435-6448Crossref PubMed Scopus (1) Google Scholar,19.Fraik A.K. et al.Disease swamps molecular signatures of genetic-environmental associations to abiotic factors in Tasmanian devil (Sarcophilus harrisii) populations.Evolution (N Y). 2020; 74: 1392-1408Crossref PubMed Scopus (13) Google Scholar]). Yet, biotic and abiotic factors often operate on different spatial and temporal scales and affect different genomic features, thus providing distinct insights into how selective forces may act in natural populations [20.Descombes P. et al.Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes.Ecography. 2020; 43: 1448-1463Crossref Scopus (24) Google Scholar]. For instance, mean ecological indicator values can be derived from floristic compositions at multiple sampling locations to ascribe not only long-term humidity, light availability, soil organic matter content, or pH conditions [21.Leempoel K. et al.Multiscale landscape genomic models to detect signatures of selection in the alpine plant Biscutella laevigata.Ecol. Evol. 2018; 8: 1794-1806Crossref PubMed Scopus (7) Google Scholar], but also biotic interactions. Of the studies employing abiotic factors, the vast majority (91.4%; Figure 1D) considered climate factors in GEA analyses, followed by topography (35.3%) and soil (11.5%) factors. This focus reflects the desire to gain knowledge on genetic patterns and molecular mechanisms of adaptation to local (micro-) climatic conditions and how they may respond to future climate change. However, recent studies frequently supplemented climate data with other environmental data (Figure 1D). For instance, Yadav et al. [22.Yadav S. et al.Microgeographical adaptation corresponds to elevational distributions of congeneric montane grasshoppers.Mol. Ecol. 2021; 30: 481-498Crossref PubMed Scopus (12) Google Scholar] investigated local adaptation of two grasshopper species endemic to the Australian Alps and found significant GEAs, particularly with long-term precipitation seasonality, number of frost days, terrain ruggedness, and soil pH conditions. This study illustrates the benefit of incorporating diverse environmental data conditioned on hypotheses to assess the multifactorial nature of local adaptation. While soil plays a key role in the establishment and persistence of many organisms, including animals, fungi, and plants, few studies have focused on below-ground environmental properties in GEA analyses because appropriate data were largely missing. Yet, great efforts have been made to generate comprehensive datasets of interpolated soil descriptors at decent spatial resolution to complement in situ measurements. For instance, a global prediction of bacterial diversity has been established to provide the first reference atlas of dominant bacteria on Earth [23.Delgado-Baquerizo M. et al.A global atlas of the dominant bacteria found in soil.Science. 2018; 325: 320-325Crossref Scopus (1072) Google Scholar], based on random forest modelling using interpolated soil data at high spatial resolution (i.e., 250 m [24.Hengl T. et al.SoilGrids250m: global gridded soil information based on machine learning.PLoS One. 2017; 12e0169748Crossref PubMed Scopus (1977) Google Scholar]). Similarly, soil variables were used to explain the global abundance of nematodes with links to soil fertility and functioning [25.van den Hoogen J. et al.Soil nematode abundance and functional group composition at a global scale.Nature. 2019; 572: 194-198Crossref PubMed Scopus (512) Google Scholar]. These chemical and physical soil factors are usually released with cross-validation scores and uncertainty maps, which help users to perceive the level of confidence and, thereby, the number of observations that support the target geographical areas (e.g., https://soilgrids.org). Recently, Lembrechts et al. [26.Lembrechts J.J. et al.Global maps of soil temperature.Glob. Chang. Biol. 2022; 28: 3110-3144Crossref PubMed Scopus (76) Google Scholar] highlighted the difference between in situ soil temperature measurements and atmospheric air temperature (up to 10°C in some areas, mean 3.0 ± 2.1°C) at a global scale, in particular in cold and dry biomes. These authors advocated the need to collect soil data in yet unsampled geographical areas to improve the quality and density of environmental data, essential for spatial interpolation. Remote sensing techniques can also help improve the modelling of soil characteristics over time, such as surface soil moisture [27.Chen Y. et al.An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003-2018.Earth Syst. Sci. Data. 2021; 13: 1-31Crossref Scopus (38) Google Scholar]. Generation time and population dynamics influence the ability to detect patterns of local adaptation (Figure 3A,B ). For example, forest tree species are known to have long generation times and produce large numbers of seeds, resulting in dense seedling layers subjected to strong selection and intraspecific competition for light and nutrients [28.Petit R.J. Hampe A. Some evolutionary consequences of being a tree.Annu. Rev. Ecol. Evol. Syst. 2006; 37: 187-214Crossref Scopus (765) Google Scholar]. Characterising sampling site conditions at the time of juvenile tree establishment, considered as the period of strongest selection (sometimes more than 100 years ago), helps to capture the adaptation-relevant environmental variation used in GEA, as shown in Swiss stone pine (Pinus cembra) [29.Dauphin B. et al.Genomic vulnerability to rapid climate warming in a tree species with a long generation time.Glob. Chang. Biol. 2021; 27: 1-15Crossref PubMed Scopus (26) Google Scholar]. This is achieved by tuning the temporal grain as well as the temporal extent of environmental data to target the desired time period (Figure 3A). In a study on the breeding range of the yellow warbler (Setophaga petechia) [5.Bay R.A. et al.Genomic signals of selection predict climate-driven population declines in a migratory bird.Science (1979). 2018; 359: 83-86Crossref PubMed Scopus (237) Google Scholar], a migratory bird with a short generation time, the authors found significant GEAs with local precipitation conditions averaged over 1970–2000, highlighting the impact of climate-induced selection on the birds' breeding habitat. Interestingly, based on the same terrestrial bioclimatological variables (reference period 1970–2000), populations of the migratory Arctic charr (Salvelinus alpinus) were found to be adapted to their local environmental conditions irrespective of breeding range and sampling years and with individuals spanning several age classes (2005–2017) [4.Layton K.K.S. et al.Genomic evidence of past and future climate-linked loss in a migratory Arctic fish.Nat. Clim. Chang. 2021; 11: 158-165Crossref Scopus (24) Google Scholar]. This time lag between the reference period of environmental predictors and sampling years (Figure 1E), intentional or not, is of primary importance because natural selection can vary across years and operate with antagonistic effects on allele frequencies in natural populations [30.Troth A. et al.Selective trade-offs maintain alleles underpinning complex trait variation in plants.Science. 2018; 361: 475-478Crossref PubMed Scopus (66) Google Scholar]. Hence, we advocate optimising the match between the temporal scales of the environmental data and the selection periods to ensure that they optimally reflect the anticipated evolutionary responses to which the sampled individuals have been exposed. With numerous geo-referenced environmental databases and knowledge of a species' life history at hand, it is possible to explore and test new adaptation-focused hypotheses by going beyond the often-chosen strategy of using conveniently available factors such as those related to bioclimatology or geography. Although there is probably no single data source that meets all needs in terms of spatial and temporal extent and grain, a set of complementary datasets can be used to capture environmental heterogeneity in space and time (Figure 3). For instance, when studying selection based on long-term climatic means versus minimum/maximum values or extreme climate events, the gradual change in average climate conditions (e.g., sum of annual precipitation for 1970–2010) could be teamed with hourly or daily data (e.g., precipitation during the growing season 2018) to assess local and singular pulses that are strong enough to induce rapid evolution within populations. In addition, as the temporal grain size commonly increases going back in time due to the diminishing number of observations, a trade-off has to be made between incorporating environmental conditions in the early life stages of long-lived organisms and the accuracy of the predictors tested in GEA. This appears to be particularly challenging for extreme events such as droughts, although there has recently been substantial improvement combining multiple independent datasets (e.g., [31.Cook E.R. et al.Old World megadroughts and pluvials during the Common Era.Sci. Adv. 2015; 1: 1-22Crossref Scopus (371) Google Scholar]). At what spatial scale selection operates seems an impractical question, as selection acts on phenotypes of single individuals and therefore has no explicit spatial scale. Instead, when planning a landscape genomic study, the focus should be on the spatial scale at which local adaptation is detectable for certain taxonomic groups and life histories, which is intimately linked to sampling strategies and the match between genetic and environmental data outlined in the next section. There is growing awareness of applying environmentally and genetically informed sampling designs [12.Selmoni O. et al.Sampling strategy optimization to increase statistical power in landscape genomics: a simulation-based approach.Mol. Ecol. Resour. 2020; 20: 154-169Crossref PubMed Scopus (22) Google Scholar]. Sampling for landscape genomic studies generally follows one of the following three strategies: (i) uninformed, (ii) environment-informed, (iii) and environment- and genetics-informed designs (Box 1), using, or not, a priori knowledge of environmental and/or genetic variation. While the alternatives within the sampling strategy (i) do not rely on environmental or genetic data, regular, scattered, or transect sampling designs are often used (67.6% of the studies surveyed; Figure 1C) as they are assumed to adequately capture both genetic diversity and environmental heterogeneity (see Figure IA in Box 1). In contrast, strategy (ii) incorporates knowledge from environmental data beforehand to establish a sampling design. Considering paired populations (e.g., wet/dry or low/high elevation) or sampling along known ecological gradient(s) strives for maximising environmental variation covered by sampling locations, while keeping a good representation of a species' distribution range (23.7%; Figures 1C and see Figure IB in Box 1). However, ecological gradients can be correlated with patterns of demographic history (e.g., [32.Yeaman S. et al.Convergent local adaptation to climate in distantly related conifers.Science. 2016; 353: 23-26Crossref Scopus (196) Google Scholar]; see later), a phenomenon that should be accounted for in sampling and downstream data analysis. In addition, although sampling of multiple paired populations is known to have high statistical power while limiting false positive associations [11.Lotterhos K.E. Whitlock M.C. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method.Mol. Ecol. 2015; 24: 1031-1046Crossref PubMed Scopus (321) Google Scholar], it is rarely implemented in the field (3.4%, but see [33.Buehler D. et al.An outlier locus relevant in habitat-mediated selection in an alpine plant across independent regional replicates.Evol. Ecol. 2013; 27: 285-300Crossref Scopus (13) Google Scholar]; Figure 1C), likely due to the limited knowledge of sites with a similar environmental contrast. Yet, such simulation-supported sampling greatly mitigates the putative confounding effect of demographic history by selecting closely related populations growing in consistently contrasting environments [11.Lotterhos K.E. Whitlock M.C. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method.Mol. Ecol. 2015; 24: 1031-1046Crossref PubMed Scopus (321) Google Scholar]. Another drawback is that many of the available environmental factors (e.g., climate data) do not have the spatial resolution to capture the environmental contrast between closely situated locations. Finally, the most informed sampling strategy (iii) takes benefit of environmental data and available genetic information to set up a suitable sampling strategy (see Figure IC in Box 1). Thus, the sampling design is optimised by incorporating both environmental variation (e.g., climatic zones) and genetic variation (e.g., genetic clusters, evolutionary lineages), usually resulting in stratified sampling for both components (see Figure IC in Box 1), with a balanced representation of individuals sharing the same demographic background. In conclusion, we anticipate that choosing a well-informed sampling design such as strategy (iii) may contribute to increased robustness of GEA outcomes.Box 1Sampling designs used in landscape genomic studiesLandscape genomic studies are generally conducted using one of the following six sampling designs, which may or may not incorporate prior knowledge of environmental and/or genetic variation from populations studied (Figure I). Definitions of these main sampling designs considered in this review, with their possible variants and examples, are next.Uninformed designRegular: sampling individuals or populations according to a regular grid.Scattered: sampling individuals or populations in an unstructured way (e.g., randomly or depending on available sampling locations).Transect: sampling individuals or populations along regular and linear Euclidian distances (e.g., every 20 km along a geographical axis).Environment-informedPaired: sampling individuals in paired populations with contrasting environmental conditions (e.g., cold and warm habitats). This is a special case of categorical sampling and pairs are usually replicated several times.Gradient: sampling individuals or populations along an environmental gradient (e.g., elevation/latitude, atmospheric temperature, soil pH conditions).Environment- and genetics-informedStratified: sampling evenly (often randomly) within clearly defined subsets of individuals or populations (strata; i.e., an equivalent number of samples per environmental cluster and genetic group). Stratification can also be conducted with environmental or genetic information only (e.g., with even numbers of populations from biogeographical regions or a fair representation of previously characterised genetic lineages). Landscape genomic studies are generally conducted using one of the following six sampling designs, which may or may not incorporate prior knowledge of environmental and/or genetic variation from populations studied (Figure I). Definitions of these main sampling designs considered in this review, with their possible variants and examples, are next. Uninformed design Regular: sampling individuals or populations according to a regular grid. Scattered: sampling individuals or populations in an unstructured way (e.g., randomly or depending on available sampling locations). Transect: sampling individuals or populations along regular and linear Euclidian distances (e.g., every 20 km along a geographical axis). Environment-informed Paired: sampling individuals in paired populations with contrasting environmental conditions (e.g., cold and warm habitats). This is a special case of categorical sampling and pairs are usually replicated several times. Gradient: sampling individuals or populations along an environmental gradient (e.g., elevation/latitude, atmospheric temperature, soil pH conditions). Environment- and genetics-informed Stratified: sampling evenly (often randomly) within clearly defined subsets of individuals or populations (strata; i.e., an equivalent number of samples per environmental cluster and genetic group). Stratification can also be conducted with environmental or genetic information only (e.g., with even numbers of populations from biogeographical regions or a fair representation of previously characterised genetic lineages). Sample size p